Compact Representation of Continuous Energy Surfaces for More Efficient Protein Design

Hallen, Mark A.; Gainza, Pablo; Donald, Bruce Randall

doi:10.1021/ct501031m

Cited by 23 publications

(52 citation statements)

References 40 publications

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“…Flexibility ranges in complexity from discrete, rigid rotamers to continuous side chain flexibility to complete flexibility including continuous backbone flexibility. c) The EPIC and LUTE algorithms also expand energy function capability by allowing for nonpairwise, basic quantum chemistry and Poisson–Boltzmann solvation. d) COMETS allows for multistate design by optimizing sequences and conformations for user‐specified bound and unbound states.…”

Section: Introductionmentioning

confidence: 99%

OSPREY 3.0: Open‐source protein redesign for you, with powerful new features

et al. 2018

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We present osprey 3.0, a new and greatly improved release of the osprey protein design software. osprey 3.0 features a convenient new Python interface, which greatly improves its ease of use. It is over two orders of magnitude faster than previous versions of osprey when running the same algorithms on the same hardware. Moreover, osprey 3.0 includes several new algorithms, which introduce substantial speedups as well as improved biophysical modeling. It also includes GPU support, which provides an additional speedup of over an order of magnitude. Like previous versions of osprey, osprey 3.0 offers a unique package of advantages over other design software, including provable design algorithms that account for continuous flexibility during design and model conformational entropy. Finally, we show here empirically that osprey 3.0 accurately predicts the effect of mutations on protein-protein binding. osprey 3.0 is available at http://www.cs.duke.edu/donaldlab/osprey.php as free and open-source software.

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Section: Introductionmentioning

confidence: 99%

OSPREY 3.0: Open‐source protein redesign for you, with powerful new features

et al. 2018

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“…These are the first six atom pairs in the PLUG matrix for the optimal voxel of a design on PDB id 1CC8 from Ref. [Color figure can be viewed at wileyonlinelibrary.com]…”

Section: Methodsmentioning

confidence: 99%

PLUG (Pruning of Local Unrealistic Geometries) removes restrictions on biophysical modeling for protein design

Hallen

2018

Proteins

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Protein design algorithms must search an enormous conformational space to identify favorable conformations. As a result, those that perform this search with guarantees of accuracy generally start with a conformational pruning step, such as dead‐end elimination (DEE). However, the mathematical assumptions of DEE‐based pruning algorithms have up to now severely restricted the biophysical model that can feasibly be used in protein design. To lift these restrictions, I propose to prune local unrealistic geometries (PLUG) using a linear programming‐based method. PLUG's biophysical model consists only of well‐known lower bounds on interatomic distances. PLUG is intended as preprocessing for energy‐based protein design calculations, whose biophysical model need not support DEE pruning. Based on 96 test cases, PLUG is at least as effective at pruning as DEE for larger protein designs—the type that most require pruning. When combined with the LUTE protein design algorithm, PLUG greatly facilitates designs that account for continuous entropy, large multistate designs with continuous flexibility, and designs with extensive continuous backbone flexibility and advanced nonpairwise energy functions. Many of these designs are tractable only with PLUG, either for empirical reasons (LUTE's machine learning step achieves an accurate fit only after PLUG pruning), or for theoretical reasons (many energy functions are fundamentally incompatible with DEE).

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“…OSPREY (Open Source Protein REdesign for You) (3, 4, 5, 6, 7, 8, 9, 10) is a state-of-the-art, free, and open-source suite of computational protein design algorithms. To date, a number of research groups have successfully used OSPREY to perform biomedically important protein designs.…”

Section: Introductionmentioning

confidence: 99%

“…We use this specific example to illustrate the more general problem of predicting resistance in drug targets in other systems. These extensions may require the modeling of backbone flexibility (6), multi-state specificity (8), faster energy functions (10), or efficient sparse approximations (9), all of which are available in OSPREY.…”

Section: Introductionmentioning

confidence: 99%

“…Since these predictions were made in (1), we have substantially improved OSPREY’s capabilities with the following algorithmic enhancements: improved backbone flexibility (6), multi-state specificity (8), fast sparse approximations (9), partitioned rotamers for improved energy bounds (35), and a computationally efficient representation of molecular-mechanics and quantum-mechanical energy functions (10). In the following Materials and Methods sections, with this system as an example, we present a protocol to predict the same SaDHFR escape mutations using the most recent release of OSPREY.…”

Section: Introductionmentioning

confidence: 99%

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OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design

Ojewole

Lowegard

Gainza

et al. 2016

Methods in Molecular Biology

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Summary Drug resistance in protein targets is an increasingly common phenomenon that reduces the efficacy of both existing and new antibiotics. However, knowledge of future resistance mutations during pre-clinical phases of drug development would enable the design of novel antibiotics that are robust against not only known resistant mutants, but also against those that have not yet been clinically observed. Computational structure-based protein design (CSPD) is a transformative field that enables the prediction of protein sequences with desired biochemical properties such as binding affinity and specificity to a target. The use of CSPD to predict previously unseen resistance mutations represents one of the frontiers of computational protein design. In a recent study (1), we used our OSPREY (Open Source Protein REdesign for You) suite of CSPD algorithms to prospectively predict resistance mutations that arise in the active site of the dihydrofolate reductase enzyme from methicillin-resistant Staphylococcus aureus (SaDHFR) in response to selective pressure from an experimental competitive inhibitor. We demonstrated that our top predicted candidates are indeed viable resistant mutants. Since that study, we have significantly enhanced the capabilities of OSPREY with not only improved modeling of backbone flexibility, but also efficient multi-state design, fast sparse approximations, partitioned rotamers for more accurate energy bounds, and a computationally efficient representation of molecular-mechanics and quantum-mechanical energy functions. Here, using SaDHFR as an example, we present a protocol for resistance prediction using the latest version of OSPREY. Specifically, we show how to use a combination of positive and negative design to predict active site escape mutations that maintain the enzyme’s catalytic function but selectively ablate binding of an inhibitor.

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Compact Representation of Continuous Energy Surfaces for More Efficient Protein Design

Cited by 23 publications

References 40 publications

OSPREY 3.0: Open‐source protein redesign for you, with powerful new features

OSPREY 3.0: Open‐source protein redesign for you, with powerful new features

PLUG (Pruning of Local Unrealistic Geometries) removes restrictions on biophysical modeling for protein design

OSPREY Predicts Resistance Mutations Using Positive and Negative Computational Protein Design

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